标题： 未来通信领域大型人工智能模型的全面调查：基础、应用与挑战 显示英文标题

标题： A Comprehensive Survey of Large AI Models for Future Communications: Foundations, Applications and Challenges

摘要： 6G无线通信旨在建立一个无处不在的智能连接世界，提供前所未有的通信体验。 大型人工智能模型（LAMs）的特点是相比于典型的AI模型，其规模显著更大（例如，数十亿或数万亿个参数）。 LAMs表现出出色的认知能力，包括强大的泛化能力，可以微调到下游任务，以及处理训练过程中未见过的任务的新兴能力。 因此，LAMs能够高效地为各种通信应用提供AI服务，使其成为解决未来无线通信系统中复杂挑战的关键工具。 本研究全面回顾了LAMs在通信中的基础、应用和挑战。 首先，我们介绍了基于AI的通信系统的现状，强调了将LAMs集成到通信中的动机，并总结了主要贡献。 然后，我们概述了通信中LAMs的基本概念。 这包括介绍LAMs的主要架构，如Transformer、扩散模型和mamba。 我们还探讨了LAMs的分类，包括大型语言模型（LLMs）、大型视觉模型（LVMs）、大型多模态模型（LMMs）和世界模型，并考察了它们在通信中的潜在应用。 此外，我们涵盖了通信系统中LAMs的训练方法和评估技术。 最后，我们介绍了优化策略，如思维链（CoT）、检索增强生成（RAG）和代理系统。 随后，我们讨论了LAMs在各种通信场景中的研究进展。 最后，我们分析了当前研究中的挑战，并对未来可能的研究方向提供了见解。 显示英文摘要

摘要： The 6G wireless communications aim to establish an intelligent world of ubiquitous connectivity, providing an unprecedented communication experience. Large artificial intelligence models (LAMs) are characterized by significantly larger scales (e.g., billions or trillions of parameters) compared to typical artificial intelligence (AI) models. LAMs exhibit outstanding cognitive abilities, including strong generalization capabilities for fine-tuning to downstream tasks, and emergent capabilities to handle tasks unseen during training. Therefore, LAMs efficiently provide AI services for diverse communication applications, making them crucial tools for addressing complex challenges in future wireless communication systems. This study provides a comprehensive review of the foundations, applications, and challenges of LAMs in communication. First, we introduce the current state of AI-based communication systems, emphasizing the motivation behind integrating LAMs into communications and summarizing the key contributions. We then present an overview of the essential concepts of LAMs in communication. This includes an introduction to the main architectures of LAMs, such as transformer, diffusion models, and mamba. We also explore the classification of LAMs, including large language models (LLMs), large vision models (LVMs), large multimodal models (LMMs), and world models, and examine their potential applications in communication. Additionally, we cover the training methods and evaluation techniques for LAMs in communication systems. Lastly, we introduce optimization strategies such as chain of thought (CoT), retrieval augmented generation (RAG), and agentic systems. Following this, we discuss the research advancements of LAMs across various communication scenarios. Finally, we analyze the challenges in the current research and provide insights into potential future research directions.